class_docstr: Class that executes a specific operation on the passed series_dict.
  
  Parameters
  ----------
  function : Callable
      The function that processes the series (given in the `series_names`).
      The prototype of the function should match: 
  
  
          function(*series: pd.Series, **kwargs)
              -> Union[np.ndarray, pd.Series, pd.DataFrame, List[pd.Series]]
  
      .. note::
          A function that processes a ``np.ndarray`` instead of ``pd.Series``
          should work just fine.
  
  series_names : Union[str, Tuple[str, ...], List[str], List[Tuple[str, ...]]]
      The names of the series on which the processing function should be applied.
  
      This argument should match the `function` its input; 
  
      * If `series_names` is a (list of) string (or tuple of a single string),
        than `function` should require just one series as input.
      * If `series_names` is a (list of) tuple of strings, than `function` should
        require `len(tuple)` series as input **and in exactly the same order**.
  
      A list means multiple series (combinations) to process; 
  
      * If `series_names` is a string or a tuple of strings, than `function` will
        be called only once for the series of this argument.
      * If `series_names` is a list of either strings or tuple of strings, than
        `function` will be called for each entry of this list.
  
      .. note::
          when passing a list as `series_names`, all items in this list should
          have the same type, i.e, either 
  
          * all a `str`
          * or, all a `tuple` _with same length_. 
  
  
  **kwargs: dict, optional
      Keyword arguments which will be also passed to the `function`
  
  Notes
  -----
  If the output of `function` is a `np.ndarray` or a `pd.Series` without a name,
  than (items of) the given `series_names` must have length 1, i.e., the function
  requires just 1 series! That series its name and index are used to transform
  (i.e., **replace**) that series.
  
  If you want to transform (i.e., **replace**) the input series with the
  processor, than `function` should return either: 
  
  * a `np.ndarray` (see above).
  * a `pd.Series` with no name or with the same name as the input series.
  * a `pd.DataFrame` with (one) column name equal to the input series its name.
  * a list of `pd.Series` in which (exact) one series has the same name as the
    input series.
  
  Series (& columns) with other (column) names will be added to the series dict.
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docstr: Return formal representation of object.
docstr_tok: ["Return","formal","representation","of","object","."]
code: def __repr__(self):
      """Return formal representation of object."""
      repr_str = self.name + (" " + str(self.kwargs))
      repr_str += " :  " + " ".join([str(s) for s in self.series_names])
      return repr_str
  
file_path: tsflex/tsflex/processing/series_processor.py
imports: import time
  from typing import Callable, Dict, List, Tuple, Union
  import numpy
  import pandas
  from ..None import __pdoc__
  from ..utils.classes import FrozenClass
  from ..utils.data import flatten, series_dict_to_df, to_list, to_tuple
  from .logger import logger
label_desc: Use image node_id 4 for calling the SeriesProcessor obj's underlying member method code with example usage: obj.__repr__() and returns: repr_str